| #!/usr/bin/env python |
| """Extracts mnist image data from the Caffe data files and stores them in numpy arrays |
| Usage |
| python caffe_mnist_image_extractor.py -d path_to_caffe_data_directory -o desired_output_path |
| |
| Saves the first 10 images extracted as input10.npy, the first 100 images as input100.npy, and the |
| corresponding labels to labels100.txt. |
| |
| Tested with Caffe 1.0 on Python 2.7 |
| """ |
| import argparse |
| import os |
| import struct |
| import numpy as np |
| from array import array |
| |
| |
| if __name__ == "__main__": |
| # Parse arguments |
| parser = argparse.ArgumentParser('Extract Caffe mnist image data') |
| parser.add_argument('-d', dest='dataDir', type=str, required=True, help='Path to Caffe data directory') |
| parser.add_argument('-o', dest='outDir', type=str, default='.', help='Output directory (default = current directory)') |
| args = parser.parse_args() |
| |
| images_filename = os.path.join(args.dataDir, 'mnist/t10k-images-idx3-ubyte') |
| labels_filename = os.path.join(args.dataDir, 'mnist/t10k-labels-idx1-ubyte') |
| |
| images_file = open(images_filename, 'rb') |
| labels_file = open(labels_filename, 'rb') |
| images_magic, images_size, rows, cols = struct.unpack('>IIII', images_file.read(16)) |
| labels_magic, labels_size = struct.unpack('>II', labels_file.read(8)) |
| images = array('B', images_file.read()) |
| labels = array('b', labels_file.read()) |
| |
| input10_path = os.path.join(args.outDir, 'input10.npy') |
| input100_path = os.path.join(args.outDir, 'input100.npy') |
| labels100_path = os.path.join(args.outDir, 'labels100.npy') |
| |
| outputs_10 = np.zeros(( 10, 28, 28, 1), dtype=np.float32) |
| outputs_100 = np.zeros((100, 28, 28, 1), dtype=np.float32) |
| labels_output = open(labels100_path, 'w') |
| for i in xrange(100): |
| image = np.array(images[i * rows * cols : (i + 1) * rows * cols]).reshape((rows, cols)) / 256.0 |
| outputs_100[i, :, :, 0] = image |
| |
| if i < 10: |
| outputs_10[i, :, :, 0] = image |
| |
| if i == 10: |
| np.save(input10_path, np.transpose(outputs_10, (0, 3, 1, 2))) |
| print "Wrote", input10_path |
| |
| labels_output.write(str(labels[i]) + '\n') |
| |
| labels_output.close() |
| print "Wrote", labels100_path |
| |
| np.save(input100_path, np.transpose(outputs_100, (0, 3, 1, 2))) |
| print "Wrote", input100_path |